Principal Component Analysis (PCA) from Scratch {https://t.co/5izwUTe1fx} #rstats #DataScience
— R-bloggers (@Rbloggers) July 18, 2022
Some Basic Concepts about Design of Experiments and How to Perform their Analysis in R {https://t.co/CBxMQKqKt2} #rstats #DataScience
— R-bloggers (@Rbloggers) July 17, 2022
Introduction to GIS and mapping in R {https://t.co/yupf5KT4My} #rstats #DataScience
— R-bloggers (@Rbloggers) July 15, 2022
How to write a function in R and apply it to a data frame using map functions from {purr} {https://t.co/0L3YldkDEL} #rstats #DataScience
— R-bloggers (@Rbloggers) July 13, 2022
Generalized Linear Models, Part I: The Logistic Model {https://t.co/pHjZU1A6wd} #rstats #DataScience
— R-bloggers (@Rbloggers) July 18, 2022
Making Predictions with Linear Regression {https://t.co/7W6kCyjZDC} #rstats #DataScience
— R-bloggers (@Rbloggers) July 16, 2022
Principal Component Analysis through Singular Value Decomposition {https://t.co/Gaj4NGTyVl} #rstats #DataScience
— R-bloggers (@Rbloggers) July 17, 2022
Detecting Duplicates (base R vs. dplyr) {https://t.co/1uw4l2OshW} #rstats #DataScience
— R-bloggers (@Rbloggers) July 17, 2022
Network Graphs in R {https://t.co/bgKJ31WW1d} #rstats #DataScience
— R-bloggers (@Rbloggers) July 13, 2022
Data Analysis Reproducibility with R and RStudio {https://t.co/YebZKyGm1h} #rstats #DataScience
— R-bloggers (@Rbloggers) July 16, 2022
How to use %in% operator in R {https://t.co/jaxPVWtyB1} #rstats #DataScience
— R-bloggers (@Rbloggers) July 19, 2022
How to Calculate Percentiles in R {https://t.co/TSXzKqxnf8} #rstats #DataScience
— R-bloggers (@Rbloggers) July 17, 2022
How to apply a transformation to multiple columns in R? {https://t.co/izAmwAP7t8} #rstats #DataScience
— R-bloggers (@Rbloggers) July 17, 2022
Principal Component Analysis (PCA) from Scratch {https://t.co/5izwUTe1fx} #rstats #DataScience
— R-bloggers (@Rbloggers) July 18, 2022
Some Basic Concepts about Design of Experiments and How to Perform their Analysis in R {https://t.co/CBxMQKqKt2} #rstats #DataScience
— R-bloggers (@Rbloggers) July 17, 2022
Packages for Exploratory Data Analysis in R {https://t.co/wXDlQF2uXx} #rstats #DataScience
— R-bloggers (@Rbloggers) July 8, 2022
An introductory course in Shiny {https://t.co/MWxFJMO4uY} #rstats #DataScience
— R-bloggers (@Rbloggers) June 26, 2022
Introduction to GIS and mapping in R {https://t.co/yupf5KT4My} #rstats #DataScience
— R-bloggers (@Rbloggers) July 15, 2022
How to write a function in R and apply it to a data frame using map functions from {purr} {https://t.co/0L3YldkDEL} #rstats #DataScience
— R-bloggers (@Rbloggers) July 13, 2022
Generalized Linear Models, Part I: The Logistic Model {https://t.co/pHjZU1A6wd} #rstats #DataScience
— R-bloggers (@Rbloggers) July 18, 2022
Making Predictions with Linear Regression {https://t.co/7W6kCyjZDC} #rstats #DataScience
— R-bloggers (@Rbloggers) July 16, 2022
Reshaping data frames using pivot functions from {tidyr} and tally from {dplyr} {https://t.co/BKWSBzgIlI} #rstats #DataScience
— R-bloggers (@Rbloggers) July 6, 2022
What To Do (And Not to Do) with Modeling Proportions/Fractional Outcomes {https://t.co/d8rTDTIAkA} #rstats #DataScience
— R-bloggers (@Rbloggers) June 27, 2022
Webscraping in R with Rvest {https://t.co/VuzCHurZQ7} #rstats #DataScience
— R-bloggers (@Rbloggers) June 25, 2022
The Poisson distribution: From basic probability theory to regression models {https://t.co/lxZvYTrLtB} #rstats #DataScience
— R-bloggers (@Rbloggers) June 23, 2022
---
title: "RBloggers Top Tweets"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(httr)
library(lubridate)
library(jsonlite)
library(purrr)
rbloggers <- fromJSON("data/rbloggers.json")
get_tweet_embed <- function(user, status_id) {
url <-
stringr::str_glue(
"https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false"
)
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Column {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Top Tweets - 7 days {.tweet-wall}
```{r}
rblog_7 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 7, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_7_html <-
map2_chr(rblog_7$screen_name, rblog_7$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_7_html}"))
```
### Top Tweets - 30 days {.tweet-wall}
```{r}
rblog_30 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 30, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_30_html <-
map2_chr(rblog_30$screen_name, rblog_30$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_30_html}"))
```